Exploring Bounded Nonparametric Ensemble Filter Impacts on Sea Ice Data Assimilation.
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| Title: | Exploring Bounded Nonparametric Ensemble Filter Impacts on Sea Ice Data Assimilation. |
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| Authors: | Riedel, Christopher P.1 (AUTHOR) criedel@ucar.edu, Wieringa, Molly M.2 (AUTHOR), Anderson, Jeffrey L.3 (AUTHOR) |
| Source: | Monthly Weather Review. Apr2025, Vol. 153 Issue 4, p637-654. 18p. |
| Subjects: | Data assimilation, Ice fields, Test systems, Simulation methods & models, Histograms, Kalman filtering, Cryosphere |
| Abstract: | Standard ensemble Kalman filter algorithms have Gaussian assumptions built into their formulations. Gaussian assumptions make these algorithms susceptible to biased solutions when prior distributions or likelihoods are non-Gaussian. Sea ice poses a unique application for testing ensemble Kalman filter algorithms because sea ice observations are nonnegative and doubly bounded, leading to non-Gaussian distributions. Four different ensemble Kalman filter algorithms are tested in observing system simulation experiments (OSSEs) to evaluate their ability to update different sea ice fields: 1) ensemble adjustment Kalman filter, 2) ensemble Kalman filter with perturbed observations, 3) rank histogram filter (RHF), and 4) bounded RHF. The bounded RHF, an extension of the standard RHF, was recently developed to properly respect bounds (singly and doubly bounded) on distributions in observation space. Compared to the other ensemble Kalman filter algorithms, the bounded RHF pulls the ensemble closer to the true value and respects the bounds. Most notably during winter when sea ice concentration is near its upper bound of one, the bounded RHF provides updates in the observation space that are more uniformly distributed around zero compared to the other algorithms. One common finding among all ensemble Kalman filter algorithms tested is the overdispersive nature of sea ice thickness. This was linked back to the method used to create the initial ensemble spread for our free forecasts in our OSSEs. Improving our ability to assimilate sea ice observations within our coupled Earth system modeling frameworks will help improve future projections of the climate and processes related to the cryosphere. [ABSTRACT FROM AUTHOR] |
| Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 184594081 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploring Bounded Nonparametric Ensemble Filter Impacts on Sea Ice Data Assimilation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Riedel%2C+Christopher+P%2E%22">Riedel, Christopher P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> criedel@ucar.edu</i><br /><searchLink fieldCode="AR" term="%22Wieringa%2C+Molly+M%2E%22">Wieringa, Molly M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Anderson%2C+Jeffrey+L%2E%22">Anderson, Jeffrey L.</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. Apr2025, Vol. 153 Issue 4, p637-654. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Ice+fields%22">Ice fields</searchLink><br /><searchLink fieldCode="DE" term="%22Test+systems%22">Test systems</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Histograms%22">Histograms</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Cryosphere%22">Cryosphere</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Standard ensemble Kalman filter algorithms have Gaussian assumptions built into their formulations. Gaussian assumptions make these algorithms susceptible to biased solutions when prior distributions or likelihoods are non-Gaussian. Sea ice poses a unique application for testing ensemble Kalman filter algorithms because sea ice observations are nonnegative and doubly bounded, leading to non-Gaussian distributions. Four different ensemble Kalman filter algorithms are tested in observing system simulation experiments (OSSEs) to evaluate their ability to update different sea ice fields: 1) ensemble adjustment Kalman filter, 2) ensemble Kalman filter with perturbed observations, 3) rank histogram filter (RHF), and 4) bounded RHF. The bounded RHF, an extension of the standard RHF, was recently developed to properly respect bounds (singly and doubly bounded) on distributions in observation space. Compared to the other ensemble Kalman filter algorithms, the bounded RHF pulls the ensemble closer to the true value and respects the bounds. Most notably during winter when sea ice concentration is near its upper bound of one, the bounded RHF provides updates in the observation space that are more uniformly distributed around zero compared to the other algorithms. One common finding among all ensemble Kalman filter algorithms tested is the overdispersive nature of sea ice thickness. This was linked back to the method used to create the initial ensemble spread for our free forecasts in our OSSEs. Improving our ability to assimilate sea ice observations within our coupled Earth system modeling frameworks will help improve future projections of the climate and processes related to the cryosphere. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/MWR-D-24-0096.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 637 Subjects: – SubjectFull: Data assimilation Type: general – SubjectFull: Ice fields Type: general – SubjectFull: Test systems Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Histograms Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Cryosphere Type: general Titles: – TitleFull: Exploring Bounded Nonparametric Ensemble Filter Impacts on Sea Ice Data Assimilation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Riedel, Christopher P. – PersonEntity: Name: NameFull: Wieringa, Molly M. – PersonEntity: Name: NameFull: Anderson, Jeffrey L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 153 – Type: issue Value: 4 Titles: – TitleFull: Monthly Weather Review Type: main |
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